An Adaptive Electronic Market-Maker

Nicholas T. Chan and Christian Shelton

No 146, Computing in Economics and Finance 2001 from Society for Computational Economics

Abstract: This paper presents an adaptive learning model for market-making under the reinforcement learn-ing framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from real-time market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building market-making algorithms.

Keywords: Agent-Based Models; Market-making; Artificial Markets; Market Microstructure (search for similar items in EconPapers)
JEL-codes: D83 G12 (search for similar items in EconPapers)
Date: 2001-04-01
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Citations: View citations in EconPapers (23)

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